investigate_data_political.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "mpl.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df = pd.read_csv(\"country_regions.csv\")\n",
    "populations_df = pd.read_csv(\"population_total.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>34.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  2012  2013  2014  2015  2016  2017\n",
       "0    Afghanistan   8.0   8.0  12.0  11.0  15.0    15\n",
       "1        Albania  33.0  31.0  33.0  36.0  39.0    38\n",
       "2        Algeria  34.0  36.0  36.0  36.0  34.0    33\n",
       "3         Angola  22.0  23.0  19.0  15.0  18.0    19\n",
       "4      Argentina  35.0  34.0  34.0  32.0  36.0    39\n",
       "..           ...   ...   ...   ...   ...   ...   ...\n",
       "172    Venezuela  19.0  20.0  19.0  17.0  17.0    18\n",
       "173      Vietnam  31.0  31.0  31.0  31.0  33.0    35\n",
       "174        Yemen  23.0  18.0  19.0  18.0  14.0    16\n",
       "175       Zambia  37.0  38.0  38.0  38.0  38.0    37\n",
       "176     Zimbabwe  20.0  21.0  21.0  21.0  22.0    22\n",
       "\n",
       "[177 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corruption_df = pd.read_csv(\"data_political/corruption_perception_index_cpi.csv\")\n",
    "corruption_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpi_df = corruption_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 197 entries, 0 to 196\n",
      "Data columns (total 12 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   geo                               197 non-null    object \n",
      " 1   name                              197 non-null    object \n",
      " 2   four_regions                      197 non-null    object \n",
      " 3   eight_regions                     197 non-null    object \n",
      " 4   six_regions                       197 non-null    object \n",
      " 5   members_oecd_g77                  196 non-null    object \n",
      " 6   Latitude                          197 non-null    float64\n",
      " 7   Longitude                         197 non-null    float64\n",
      " 8   UN member since                   193 non-null    object \n",
      " 9   World bank region                 196 non-null    object \n",
      " 10  World bank, 4 income groups 2017  196 non-null    object \n",
      " 11  World bank, 3 income groups 2017  0 non-null      float64\n",
      "dtypes: float64(3), object(9)\n",
      "memory usage: 18.6+ KB\n"
     ]
    }
   ],
   "source": [
    "country_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Columns: 302 entries, country to 2100\n",
      "dtypes: int64(301), object(1)\n",
      "memory usage: 460.2+ KB\n"
     ]
    }
   ],
   "source": [
    "populations_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1800</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>1804</th>\n",
       "      <th>1805</th>\n",
       "      <th>1806</th>\n",
       "      <th>1807</th>\n",
       "      <th>1808</th>\n",
       "      <th>...</th>\n",
       "      <th>2091</th>\n",
       "      <th>2092</th>\n",
       "      <th>2093</th>\n",
       "      <th>2094</th>\n",
       "      <th>2095</th>\n",
       "      <th>2096</th>\n",
       "      <th>2097</th>\n",
       "      <th>2098</th>\n",
       "      <th>2099</th>\n",
       "      <th>2100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>...</td>\n",
       "      <td>76600000</td>\n",
       "      <td>76400000</td>\n",
       "      <td>76300000</td>\n",
       "      <td>76100000</td>\n",
       "      <td>76000000</td>\n",
       "      <td>75800000</td>\n",
       "      <td>75600000</td>\n",
       "      <td>75400000</td>\n",
       "      <td>75200000</td>\n",
       "      <td>74900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>400000</td>\n",
       "      <td>402000</td>\n",
       "      <td>404000</td>\n",
       "      <td>405000</td>\n",
       "      <td>407000</td>\n",
       "      <td>409000</td>\n",
       "      <td>411000</td>\n",
       "      <td>413000</td>\n",
       "      <td>414000</td>\n",
       "      <td>...</td>\n",
       "      <td>1330000</td>\n",
       "      <td>1300000</td>\n",
       "      <td>1270000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>1220000</td>\n",
       "      <td>1190000</td>\n",
       "      <td>1170000</td>\n",
       "      <td>1140000</td>\n",
       "      <td>1110000</td>\n",
       "      <td>1090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2500000</td>\n",
       "      <td>2510000</td>\n",
       "      <td>2520000</td>\n",
       "      <td>2530000</td>\n",
       "      <td>2540000</td>\n",
       "      <td>2550000</td>\n",
       "      <td>2560000</td>\n",
       "      <td>2560000</td>\n",
       "      <td>2570000</td>\n",
       "      <td>...</td>\n",
       "      <td>70400000</td>\n",
       "      <td>70500000</td>\n",
       "      <td>70500000</td>\n",
       "      <td>70600000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 302 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country     1800     1801     1802     1803     1804     1805     1806  \\\n",
       "0  Afghanistan  3280000  3280000  3280000  3280000  3280000  3280000  3280000   \n",
       "1      Albania   400000   402000   404000   405000   407000   409000   411000   \n",
       "2      Algeria  2500000  2510000  2520000  2530000  2540000  2550000  2560000   \n",
       "\n",
       "      1807     1808  ...      2091      2092      2093      2094      2095  \\\n",
       "0  3280000  3280000  ...  76600000  76400000  76300000  76100000  76000000   \n",
       "1   413000   414000  ...   1330000   1300000   1270000   1250000   1220000   \n",
       "2  2560000  2570000  ...  70400000  70500000  70500000  70600000  70700000   \n",
       "\n",
       "       2096      2097      2098      2099      2100  \n",
       "0  75800000  75600000  75400000  75200000  74900000  \n",
       "1   1190000   1170000   1140000   1110000   1090000  \n",
       "2  70700000  70700000  70700000  70700000  70700000  \n",
       "\n",
       "[3 rows x 302 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df.drop(columns=['World bank, 3 income groups 2017'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df.drop(columns=['UN member since','members_oecd_g77','geo','World bank region','World bank, 4 income groups 2017'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>four_regions</th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>six_regions</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>asia</td>\n",
       "      <td>asia_west</td>\n",
       "      <td>south_asia</td>\n",
       "      <td>33.00000</td>\n",
       "      <td>66.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>20.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_north</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_west</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>42.50779</td>\n",
       "      <td>1.52109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-12.50000</td>\n",
       "      <td>18.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>asia</td>\n",
       "      <td>east_asia_pacific</td>\n",
       "      <td>east_asia_pacific</td>\n",
       "      <td>16.16667</td>\n",
       "      <td>107.83333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>asia</td>\n",
       "      <td>asia_west</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>15.50000</td>\n",
       "      <td>47.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-14.33333</td>\n",
       "      <td>28.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-19.00000</td>\n",
       "      <td>29.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>7.50000</td>\n",
       "      <td>30.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country four_regions       eight_regions               six_regions  \\\n",
       "0    Afghanistan         asia           asia_west                south_asia   \n",
       "1        Albania       europe         europe_east       europe_central_asia   \n",
       "2        Algeria       africa        africa_north  middle_east_north_africa   \n",
       "3        Andorra       europe         europe_west       europe_central_asia   \n",
       "4         Angola       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "..           ...          ...                 ...                       ...   \n",
       "192      Vietnam         asia   east_asia_pacific         east_asia_pacific   \n",
       "193        Yemen         asia           asia_west  middle_east_north_africa   \n",
       "194       Zambia       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "195     Zimbabwe       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "196  South Sudan       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "\n",
       "     Latitude  Longitude  \n",
       "0    33.00000   66.00000  \n",
       "1    41.00000   20.00000  \n",
       "2    28.00000    3.00000  \n",
       "3    42.50779    1.52109  \n",
       "4   -12.50000   18.50000  \n",
       "..        ...        ...  \n",
       "192  16.16667  107.83333  \n",
       "193  15.50000   47.50000  \n",
       "194 -14.33333   28.50000  \n",
       "195 -19.00000   29.75000  \n",
       "196   7.50000   30.00000  \n",
       "\n",
       "[197 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df.rename(columns={'name':'country'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 197 entries, 0 to 196\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   name           197 non-null    object \n",
      " 1   four_regions   197 non-null    object \n",
      " 2   eight_regions  197 non-null    object \n",
      " 3   six_regions    197 non-null    object \n",
      " 4   Latitude       197 non-null    float64\n",
      " 5   Longitude      197 non-null    float64\n",
      "dtypes: float64(2), object(4)\n",
      "memory usage: 9.4+ KB\n"
     ]
    }
   ],
   "source": [
    "country_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations_df['country'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df.rename(columns={'name':'country'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>four_regions</th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>six_regions</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>asia</td>\n",
       "      <td>asia_west</td>\n",
       "      <td>south_asia</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country four_regions eight_regions          six_regions  Latitude  \\\n",
       "0  Afghanistan         asia     asia_west           south_asia      33.0   \n",
       "1      Albania       europe   europe_east  europe_central_asia      41.0   \n",
       "\n",
       "   Longitude  \n",
       "0       66.0  \n",
       "1       20.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CPI - Corruption Perception Index - Transparency International's score of perceptions of corruption. Higher values indicate LESS corruption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpiregions_df = pd.merge(country_df,cpi_df,on='country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>four_regions</th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>six_regions</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>asia</td>\n",
       "      <td>asia_west</td>\n",
       "      <td>south_asia</td>\n",
       "      <td>33.00000</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_north</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-12.50000</td>\n",
       "      <td>18.50000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>americas</td>\n",
       "      <td>america_south</td>\n",
       "      <td>america</td>\n",
       "      <td>-34.00000</td>\n",
       "      <td>-64.00000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>asia</td>\n",
       "      <td>east_asia_pacific</td>\n",
       "      <td>east_asia_pacific</td>\n",
       "      <td>16.16667</td>\n",
       "      <td>107.83333</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>asia</td>\n",
       "      <td>asia_west</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>15.50000</td>\n",
       "      <td>47.50000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-14.33333</td>\n",
       "      <td>28.50000</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-19.00000</td>\n",
       "      <td>29.75000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>7.50000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>176 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country four_regions       eight_regions               six_regions  \\\n",
       "0    Afghanistan         asia           asia_west                south_asia   \n",
       "1        Albania       europe         europe_east       europe_central_asia   \n",
       "2        Algeria       africa        africa_north  middle_east_north_africa   \n",
       "3         Angola       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "4      Argentina     americas       america_south                   america   \n",
       "..           ...          ...                 ...                       ...   \n",
       "171      Vietnam         asia   east_asia_pacific         east_asia_pacific   \n",
       "172        Yemen         asia           asia_west  middle_east_north_africa   \n",
       "173       Zambia       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "174     Zimbabwe       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "175  South Sudan       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "\n",
       "     Latitude  Longitude  2012  2013  2014  2015  2016  2017  \n",
       "0    33.00000   66.00000   8.0   8.0  12.0  11.0  15.0    15  \n",
       "1    41.00000   20.00000  33.0  31.0  33.0  36.0  39.0    38  \n",
       "2    28.00000    3.00000  34.0  36.0  36.0  36.0  34.0    33  \n",
       "3   -12.50000   18.50000  22.0  23.0  19.0  15.0  18.0    19  \n",
       "4   -34.00000  -64.00000  35.0  34.0  34.0  32.0  36.0    39  \n",
       "..        ...        ...   ...   ...   ...   ...   ...   ...  \n",
       "171  16.16667  107.83333  31.0  31.0  31.0  31.0  33.0    35  \n",
       "172  15.50000   47.50000  23.0  18.0  19.0  18.0  14.0    16  \n",
       "173 -14.33333   28.50000  37.0  38.0  38.0  38.0  38.0    37  \n",
       "174 -19.00000   29.75000  20.0  21.0  21.0  21.0  22.0    22  \n",
       "175   7.50000   30.00000   NaN  14.0  15.0  15.0  11.0    12  \n",
       "\n",
       "[176 rows x 12 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpiregions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eight_regions</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>africa_north</th>\n",
       "      <td>33.500000</td>\n",
       "      <td>32.833333</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>32.666667</td>\n",
       "      <td>31.666667</td>\n",
       "      <td>32.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <td>33.297872</td>\n",
       "      <td>32.041667</td>\n",
       "      <td>32.702128</td>\n",
       "      <td>32.260870</td>\n",
       "      <td>31.488889</td>\n",
       "      <td>32.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_north</th>\n",
       "      <td>48.684211</td>\n",
       "      <td>47.578947</td>\n",
       "      <td>48.315789</td>\n",
       "      <td>41.857143</td>\n",
       "      <td>46.800000</td>\n",
       "      <td>46.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_south</th>\n",
       "      <td>39.250000</td>\n",
       "      <td>39.166667</td>\n",
       "      <td>39.583333</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>39.583333</td>\n",
       "      <td>39.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asia_west</th>\n",
       "      <td>35.600000</td>\n",
       "      <td>34.680000</td>\n",
       "      <td>35.760000</td>\n",
       "      <td>36.440000</td>\n",
       "      <td>35.346154</td>\n",
       "      <td>35.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <td>43.631579</td>\n",
       "      <td>44.157895</td>\n",
       "      <td>43.444444</td>\n",
       "      <td>43.277778</td>\n",
       "      <td>44.950000</td>\n",
       "      <td>45.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_east</th>\n",
       "      <td>45.086957</td>\n",
       "      <td>45.391304</td>\n",
       "      <td>45.869565</td>\n",
       "      <td>46.434783</td>\n",
       "      <td>47.043478</td>\n",
       "      <td>47.173913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_west</th>\n",
       "      <td>70.950000</td>\n",
       "      <td>70.800000</td>\n",
       "      <td>71.550000</td>\n",
       "      <td>72.900000</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>71.550000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         2012       2013       2014       2015       2016  \\\n",
       "eight_regions                                                               \n",
       "africa_north        33.500000  32.833333  34.000000  32.666667  31.666667   \n",
       "africa_sub_saharan  33.297872  32.041667  32.702128  32.260870  31.488889   \n",
       "america_north       48.684211  47.578947  48.315789  41.857143  46.800000   \n",
       "america_south       39.250000  39.166667  39.583333  38.500000  39.583333   \n",
       "asia_west           35.600000  34.680000  35.760000  36.440000  35.346154   \n",
       "east_asia_pacific   43.631579  44.157895  43.444444  43.277778  44.950000   \n",
       "europe_east         45.086957  45.391304  45.869565  46.434783  47.043478   \n",
       "europe_west         70.950000  70.800000  71.550000  72.900000  71.500000   \n",
       "\n",
       "                         2017  \n",
       "eight_regions                  \n",
       "africa_north        32.500000  \n",
       "africa_sub_saharan  32.041667  \n",
       "america_north       46.800000  \n",
       "america_south       39.833333  \n",
       "asia_west           35.538462  \n",
       "east_asia_pacific   45.333333  \n",
       "europe_east         47.173913  \n",
       "europe_west         71.550000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpi_regions_mean = cpiregions_df.groupby('eight_regions')[['2012','2013','2014','2015','2016','2017']].mean()\n",
    "cpi_regions_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>four_regions</th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>six_regions</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>1800</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>...</th>\n",
       "      <th>2091</th>\n",
       "      <th>2092</th>\n",
       "      <th>2093</th>\n",
       "      <th>2094</th>\n",
       "      <th>2095</th>\n",
       "      <th>2096</th>\n",
       "      <th>2097</th>\n",
       "      <th>2098</th>\n",
       "      <th>2099</th>\n",
       "      <th>2100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>asia</td>\n",
       "      <td>asia_west</td>\n",
       "      <td>south_asia</td>\n",
       "      <td>33.00000</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>3280000</td>\n",
       "      <td>...</td>\n",
       "      <td>76600000</td>\n",
       "      <td>76400000</td>\n",
       "      <td>76300000</td>\n",
       "      <td>76100000</td>\n",
       "      <td>76000000</td>\n",
       "      <td>75800000</td>\n",
       "      <td>75600000</td>\n",
       "      <td>75400000</td>\n",
       "      <td>75200000</td>\n",
       "      <td>74900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>400000</td>\n",
       "      <td>402000</td>\n",
       "      <td>404000</td>\n",
       "      <td>405000</td>\n",
       "      <td>...</td>\n",
       "      <td>1330000</td>\n",
       "      <td>1300000</td>\n",
       "      <td>1270000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>1220000</td>\n",
       "      <td>1190000</td>\n",
       "      <td>1170000</td>\n",
       "      <td>1140000</td>\n",
       "      <td>1110000</td>\n",
       "      <td>1090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_north</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2500000</td>\n",
       "      <td>2510000</td>\n",
       "      <td>2520000</td>\n",
       "      <td>2530000</td>\n",
       "      <td>...</td>\n",
       "      <td>70400000</td>\n",
       "      <td>70500000</td>\n",
       "      <td>70500000</td>\n",
       "      <td>70600000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_west</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>42.50779</td>\n",
       "      <td>1.52109</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>...</td>\n",
       "      <td>63000</td>\n",
       "      <td>62900</td>\n",
       "      <td>62900</td>\n",
       "      <td>62800</td>\n",
       "      <td>62700</td>\n",
       "      <td>62700</td>\n",
       "      <td>62600</td>\n",
       "      <td>62500</td>\n",
       "      <td>62500</td>\n",
       "      <td>62400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-12.50000</td>\n",
       "      <td>18.50000</td>\n",
       "      <td>1570000</td>\n",
       "      <td>1570000</td>\n",
       "      <td>1570000</td>\n",
       "      <td>1570000</td>\n",
       "      <td>...</td>\n",
       "      <td>168000000</td>\n",
       "      <td>170000000</td>\n",
       "      <td>172000000</td>\n",
       "      <td>175000000</td>\n",
       "      <td>177000000</td>\n",
       "      <td>179000000</td>\n",
       "      <td>182000000</td>\n",
       "      <td>184000000</td>\n",
       "      <td>186000000</td>\n",
       "      <td>188000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>asia</td>\n",
       "      <td>east_asia_pacific</td>\n",
       "      <td>east_asia_pacific</td>\n",
       "      <td>16.16667</td>\n",
       "      <td>107.83333</td>\n",
       "      <td>4000000</td>\n",
       "      <td>4100000</td>\n",
       "      <td>4200000</td>\n",
       "      <td>4310000</td>\n",
       "      <td>...</td>\n",
       "      <td>100000000</td>\n",
       "      <td>100000000</td>\n",
       "      <td>99700000</td>\n",
       "      <td>99400000</td>\n",
       "      <td>99100000</td>\n",
       "      <td>98800000</td>\n",
       "      <td>98400000</td>\n",
       "      <td>98100000</td>\n",
       "      <td>97800000</td>\n",
       "      <td>97400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>asia</td>\n",
       "      <td>asia_west</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>15.50000</td>\n",
       "      <td>47.50000</td>\n",
       "      <td>2590000</td>\n",
       "      <td>2590000</td>\n",
       "      <td>2590000</td>\n",
       "      <td>2590000</td>\n",
       "      <td>...</td>\n",
       "      <td>54500000</td>\n",
       "      <td>54400000</td>\n",
       "      <td>54300000</td>\n",
       "      <td>54100000</td>\n",
       "      <td>54000000</td>\n",
       "      <td>53800000</td>\n",
       "      <td>53700000</td>\n",
       "      <td>53500000</td>\n",
       "      <td>53400000</td>\n",
       "      <td>53200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-14.33333</td>\n",
       "      <td>28.50000</td>\n",
       "      <td>747000</td>\n",
       "      <td>758000</td>\n",
       "      <td>770000</td>\n",
       "      <td>782000</td>\n",
       "      <td>...</td>\n",
       "      <td>74400000</td>\n",
       "      <td>75200000</td>\n",
       "      <td>76000000</td>\n",
       "      <td>76800000</td>\n",
       "      <td>77600000</td>\n",
       "      <td>78400000</td>\n",
       "      <td>79200000</td>\n",
       "      <td>80000000</td>\n",
       "      <td>80800000</td>\n",
       "      <td>81500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-19.00000</td>\n",
       "      <td>29.75000</td>\n",
       "      <td>1090000</td>\n",
       "      <td>1090000</td>\n",
       "      <td>1090000</td>\n",
       "      <td>1090000</td>\n",
       "      <td>...</td>\n",
       "      <td>30800000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>31000000</td>\n",
       "      <td>31000000</td>\n",
       "      <td>31000000</td>\n",
       "      <td>31000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>7.50000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>1030000</td>\n",
       "      <td>1030000</td>\n",
       "      <td>1030000</td>\n",
       "      <td>1030000</td>\n",
       "      <td>...</td>\n",
       "      <td>30500000</td>\n",
       "      <td>30600000</td>\n",
       "      <td>30800000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>31100000</td>\n",
       "      <td>31200000</td>\n",
       "      <td>31400000</td>\n",
       "      <td>31500000</td>\n",
       "      <td>31600000</td>\n",
       "      <td>31700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194 rows × 307 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country four_regions       eight_regions               six_regions  \\\n",
       "0    Afghanistan         asia           asia_west                south_asia   \n",
       "1        Albania       europe         europe_east       europe_central_asia   \n",
       "2        Algeria       africa        africa_north  middle_east_north_africa   \n",
       "3        Andorra       europe         europe_west       europe_central_asia   \n",
       "4         Angola       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "..           ...          ...                 ...                       ...   \n",
       "189      Vietnam         asia   east_asia_pacific         east_asia_pacific   \n",
       "190        Yemen         asia           asia_west  middle_east_north_africa   \n",
       "191       Zambia       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "192     Zimbabwe       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "193  South Sudan       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "\n",
       "     Latitude  Longitude     1800     1801     1802     1803  ...       2091  \\\n",
       "0    33.00000   66.00000  3280000  3280000  3280000  3280000  ...   76600000   \n",
       "1    41.00000   20.00000   400000   402000   404000   405000  ...    1330000   \n",
       "2    28.00000    3.00000  2500000  2510000  2520000  2530000  ...   70400000   \n",
       "3    42.50779    1.52109     2650     2650     2650     2650  ...      63000   \n",
       "4   -12.50000   18.50000  1570000  1570000  1570000  1570000  ...  168000000   \n",
       "..        ...        ...      ...      ...      ...      ...  ...        ...   \n",
       "189  16.16667  107.83333  4000000  4100000  4200000  4310000  ...  100000000   \n",
       "190  15.50000   47.50000  2590000  2590000  2590000  2590000  ...   54500000   \n",
       "191 -14.33333   28.50000   747000   758000   770000   782000  ...   74400000   \n",
       "192 -19.00000   29.75000  1090000  1090000  1090000  1090000  ...   30800000   \n",
       "193   7.50000   30.00000  1030000  1030000  1030000  1030000  ...   30500000   \n",
       "\n",
       "          2092       2093       2094       2095       2096       2097  \\\n",
       "0     76400000   76300000   76100000   76000000   75800000   75600000   \n",
       "1      1300000    1270000    1250000    1220000    1190000    1170000   \n",
       "2     70500000   70500000   70600000   70700000   70700000   70700000   \n",
       "3        62900      62900      62800      62700      62700      62600   \n",
       "4    170000000  172000000  175000000  177000000  179000000  182000000   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "189  100000000   99700000   99400000   99100000   98800000   98400000   \n",
       "190   54400000   54300000   54100000   54000000   53800000   53700000   \n",
       "191   75200000   76000000   76800000   77600000   78400000   79200000   \n",
       "192   30900000   30900000   30900000   30900000   30900000   31000000   \n",
       "193   30600000   30800000   30900000   31100000   31200000   31400000   \n",
       "\n",
       "          2098       2099       2100  \n",
       "0     75400000   75200000   74900000  \n",
       "1      1140000    1110000    1090000  \n",
       "2     70700000   70700000   70700000  \n",
       "3        62500      62500      62400  \n",
       "4    184000000  186000000  188000000  \n",
       "..         ...        ...        ...  \n",
       "189   98100000   97800000   97400000  \n",
       "190   53500000   53400000   53200000  \n",
       "191   80000000   80800000   81500000  \n",
       "192   31000000   31000000   31000000  \n",
       "193   31500000   31600000   31700000  \n",
       "\n",
       "[194 rows x 307 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df = pd.merge(country_df,populations_df,on='country')\n",
    "master_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>asia</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>400000</td>\n",
       "      <td>402000</td>\n",
       "      <td>404000</td>\n",
       "      <td>405000</td>\n",
       "      <td>...</td>\n",
       "      <td>1330000</td>\n",
       "      <td>1300000</td>\n",
       "      <td>1270000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>1220000</td>\n",
       "      <td>1190000</td>\n",
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       "      <td>1140000</td>\n",
       "      <td>1110000</td>\n",
       "      <td>1090000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_north</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>3.00000</td>\n",
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       "      <td>2510000</td>\n",
       "      <td>2520000</td>\n",
       "      <td>2530000</td>\n",
       "      <td>...</td>\n",
       "      <td>70400000</td>\n",
       "      <td>70500000</td>\n",
       "      <td>70500000</td>\n",
       "      <td>70600000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>70700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_west</td>\n",
       "      <td>europe_central_asia</td>\n",
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       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
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       "      <td>62700</td>\n",
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       "      <td>62500</td>\n",
       "      <td>62400</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-12.50000</td>\n",
       "      <td>18.50000</td>\n",
       "      <td>1570000</td>\n",
       "      <td>1570000</td>\n",
       "      <td>1570000</td>\n",
       "      <td>1570000</td>\n",
       "      <td>...</td>\n",
       "      <td>168000000</td>\n",
       "      <td>170000000</td>\n",
       "      <td>172000000</td>\n",
       "      <td>175000000</td>\n",
       "      <td>177000000</td>\n",
       "      <td>179000000</td>\n",
       "      <td>182000000</td>\n",
       "      <td>184000000</td>\n",
       "      <td>186000000</td>\n",
       "      <td>188000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>asia</td>\n",
       "      <td>east_asia_pacific</td>\n",
       "      <td>east_asia_pacific</td>\n",
       "      <td>16.16667</td>\n",
       "      <td>107.83333</td>\n",
       "      <td>4000000</td>\n",
       "      <td>4100000</td>\n",
       "      <td>4200000</td>\n",
       "      <td>4310000</td>\n",
       "      <td>...</td>\n",
       "      <td>100000000</td>\n",
       "      <td>100000000</td>\n",
       "      <td>99700000</td>\n",
       "      <td>99400000</td>\n",
       "      <td>99100000</td>\n",
       "      <td>98800000</td>\n",
       "      <td>98400000</td>\n",
       "      <td>98100000</td>\n",
       "      <td>97800000</td>\n",
       "      <td>97400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>asia</td>\n",
       "      <td>asia_west</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>15.50000</td>\n",
       "      <td>47.50000</td>\n",
       "      <td>2590000</td>\n",
       "      <td>2590000</td>\n",
       "      <td>2590000</td>\n",
       "      <td>2590000</td>\n",
       "      <td>...</td>\n",
       "      <td>54500000</td>\n",
       "      <td>54400000</td>\n",
       "      <td>54300000</td>\n",
       "      <td>54100000</td>\n",
       "      <td>54000000</td>\n",
       "      <td>53800000</td>\n",
       "      <td>53700000</td>\n",
       "      <td>53500000</td>\n",
       "      <td>53400000</td>\n",
       "      <td>53200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-14.33333</td>\n",
       "      <td>28.50000</td>\n",
       "      <td>747000</td>\n",
       "      <td>758000</td>\n",
       "      <td>770000</td>\n",
       "      <td>782000</td>\n",
       "      <td>...</td>\n",
       "      <td>74400000</td>\n",
       "      <td>75200000</td>\n",
       "      <td>76000000</td>\n",
       "      <td>76800000</td>\n",
       "      <td>77600000</td>\n",
       "      <td>78400000</td>\n",
       "      <td>79200000</td>\n",
       "      <td>80000000</td>\n",
       "      <td>80800000</td>\n",
       "      <td>81500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-19.00000</td>\n",
       "      <td>29.75000</td>\n",
       "      <td>1090000</td>\n",
       "      <td>1090000</td>\n",
       "      <td>1090000</td>\n",
       "      <td>1090000</td>\n",
       "      <td>...</td>\n",
       "      <td>30800000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>31000000</td>\n",
       "      <td>31000000</td>\n",
       "      <td>31000000</td>\n",
       "      <td>31000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>7.50000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>1030000</td>\n",
       "      <td>1030000</td>\n",
       "      <td>1030000</td>\n",
       "      <td>1030000</td>\n",
       "      <td>...</td>\n",
       "      <td>30500000</td>\n",
       "      <td>30600000</td>\n",
       "      <td>30800000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>31100000</td>\n",
       "      <td>31200000</td>\n",
       "      <td>31400000</td>\n",
       "      <td>31500000</td>\n",
       "      <td>31600000</td>\n",
       "      <td>31700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194 rows × 307 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country four_regions       eight_regions               six_regions  \\\n",
       "0    Afghanistan         asia           asia_west                south_asia   \n",
       "1        Albania       europe         europe_east       europe_central_asia   \n",
       "2        Algeria       africa        africa_north  middle_east_north_africa   \n",
       "3        Andorra       europe         europe_west       europe_central_asia   \n",
       "4         Angola       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "..           ...          ...                 ...                       ...   \n",
       "189      Vietnam         asia   east_asia_pacific         east_asia_pacific   \n",
       "190        Yemen         asia           asia_west  middle_east_north_africa   \n",
       "191       Zambia       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "192     Zimbabwe       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "193  South Sudan       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "\n",
       "     Latitude  Longitude     1800     1801     1802     1803  ...       2091  \\\n",
       "0    33.00000   66.00000  3280000  3280000  3280000  3280000  ...   76600000   \n",
       "1    41.00000   20.00000   400000   402000   404000   405000  ...    1330000   \n",
       "2    28.00000    3.00000  2500000  2510000  2520000  2530000  ...   70400000   \n",
       "3    42.50779    1.52109     2650     2650     2650     2650  ...      63000   \n",
       "4   -12.50000   18.50000  1570000  1570000  1570000  1570000  ...  168000000   \n",
       "..        ...        ...      ...      ...      ...      ...  ...        ...   \n",
       "189  16.16667  107.83333  4000000  4100000  4200000  4310000  ...  100000000   \n",
       "190  15.50000   47.50000  2590000  2590000  2590000  2590000  ...   54500000   \n",
       "191 -14.33333   28.50000   747000   758000   770000   782000  ...   74400000   \n",
       "192 -19.00000   29.75000  1090000  1090000  1090000  1090000  ...   30800000   \n",
       "193   7.50000   30.00000  1030000  1030000  1030000  1030000  ...   30500000   \n",
       "\n",
       "          2092       2093       2094       2095       2096       2097  \\\n",
       "0     76400000   76300000   76100000   76000000   75800000   75600000   \n",
       "1      1300000    1270000    1250000    1220000    1190000    1170000   \n",
       "2     70500000   70500000   70600000   70700000   70700000   70700000   \n",
       "3        62900      62900      62800      62700      62700      62600   \n",
       "4    170000000  172000000  175000000  177000000  179000000  182000000   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "189  100000000   99700000   99400000   99100000   98800000   98400000   \n",
       "190   54400000   54300000   54100000   54000000   53800000   53700000   \n",
       "191   75200000   76000000   76800000   77600000   78400000   79200000   \n",
       "192   30900000   30900000   30900000   30900000   30900000   31000000   \n",
       "193   30600000   30800000   30900000   31100000   31200000   31400000   \n",
       "\n",
       "          2098       2099       2100  \n",
       "0     75400000   75200000   74900000  \n",
       "1      1140000    1110000    1090000  \n",
       "2     70700000   70700000   70700000  \n",
       "3        62500      62500      62400  \n",
       "4    184000000  186000000  188000000  \n",
       "..         ...        ...        ...  \n",
       "189   98100000   97800000   97400000  \n",
       "190   53500000   53400000   53200000  \n",
       "191   80000000   80800000   81500000  \n",
       "192   31000000   31000000   31000000  \n",
       "193   31500000   31600000   31700000  \n",
       "\n",
       "[194 rows x 307 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_df = master_df\n",
    "population_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gini shows income inequality in a society. A higher number means more inequality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "gini_df = pd.read_csv(\"data_political/gini.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1800</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>1804</th>\n",
       "      <th>1805</th>\n",
       "      <th>1806</th>\n",
       "      <th>1807</th>\n",
       "      <th>1808</th>\n",
       "      <th>...</th>\n",
       "      <th>2031</th>\n",
       "      <th>2032</th>\n",
       "      <th>2033</th>\n",
       "      <th>2034</th>\n",
       "      <th>2035</th>\n",
       "      <th>2036</th>\n",
       "      <th>2037</th>\n",
       "      <th>2038</th>\n",
       "      <th>2039</th>\n",
       "      <th>2040</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>...</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>...</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>...</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>...</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>...</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>...</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>...</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  1800  1801  1802  1803  1804  1805  1806  1807  1808  \\\n",
       "0          Afghanistan  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5   \n",
       "1              Albania  38.9  38.9  38.9  38.9  38.9  38.9  38.9  38.9  38.9   \n",
       "2              Algeria  56.2  56.2  56.2  56.2  56.2  56.2  56.2  56.2  56.2   \n",
       "3              Andorra  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0   \n",
       "4               Angola  57.2  57.2  57.2  57.2  57.2  57.2  57.2  57.2  57.2   \n",
       "5  Antigua and Barbuda  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0   \n",
       "6            Argentina  47.7  47.7  47.7  47.7  47.7  47.7  47.7  47.7  47.7   \n",
       "7              Armenia  31.5  31.5  31.5  31.5  31.5  31.5  31.5  31.5  31.5   \n",
       "8            Australia  38.7  38.7  38.7  38.7  38.7  38.7  38.7  38.7  38.7   \n",
       "9              Austria  33.4  33.4  33.4  33.4  33.4  33.4  33.4  33.4  33.4   \n",
       "\n",
       "   ...  2031  2032  2033  2034  2035  2036  2037  2038  2039  2040  \n",
       "0  ...  36.8  36.8  36.8  36.8  36.8  36.8  36.8  36.8  36.8  36.8  \n",
       "1  ...  29.0  29.0  29.0  29.0  29.0  29.0  29.0  29.0  29.0  29.0  \n",
       "2  ...  27.6  27.6  27.6  27.6  27.6  27.6  27.6  27.6  27.6  27.6  \n",
       "3  ...  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  \n",
       "4  ...  42.6  42.6  42.6  42.6  42.6  42.6  42.6  42.6  42.6  42.6  \n",
       "5  ...  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  \n",
       "6  ...  42.4  42.4  42.4  42.4  42.4  42.4  42.4  42.4  42.4  42.4  \n",
       "7  ...  32.6  32.6  32.6  32.6  32.6  32.6  32.6  32.6  32.6  32.6  \n",
       "8  ...  32.3  32.3  32.3  32.3  32.3  32.3  32.3  32.3  32.3  32.3  \n",
       "9  ...  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5  \n",
       "\n",
       "[10 rows x 242 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "gini_country_df = pd.merge(country_df,gini_df,on='country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1800</th>\n",
       "      <th>1850</th>\n",
       "      <th>1900</th>\n",
       "      <th>1950</th>\n",
       "      <th>2000</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eight_regions</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>africa_north</th>\n",
       "      <td>49.383333</td>\n",
       "      <td>51.816667</td>\n",
       "      <td>35.533333</td>\n",
       "      <td>37.983333</td>\n",
       "      <td>37.683333</td>\n",
       "      <td>36.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <td>43.702083</td>\n",
       "      <td>42.935417</td>\n",
       "      <td>41.112500</td>\n",
       "      <td>43.022917</td>\n",
       "      <td>44.754167</td>\n",
       "      <td>43.495833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_north</th>\n",
       "      <td>42.986957</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>43.091304</td>\n",
       "      <td>44.095652</td>\n",
       "      <td>46.295652</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_south</th>\n",
       "      <td>47.550000</td>\n",
       "      <td>46.441667</td>\n",
       "      <td>45.883333</td>\n",
       "      <td>45.208333</td>\n",
       "      <td>53.250000</td>\n",
       "      <td>47.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asia_west</th>\n",
       "      <td>38.096296</td>\n",
       "      <td>39.062963</td>\n",
       "      <td>35.014815</td>\n",
       "      <td>36.940741</td>\n",
       "      <td>37.222222</td>\n",
       "      <td>36.096296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <td>37.363333</td>\n",
       "      <td>36.613333</td>\n",
       "      <td>37.593333</td>\n",
       "      <td>38.520000</td>\n",
       "      <td>38.623333</td>\n",
       "      <td>37.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_east</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>38.056522</td>\n",
       "      <td>34.930435</td>\n",
       "      <td>30.513043</td>\n",
       "      <td>32.239130</td>\n",
       "      <td>31.460870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_west</th>\n",
       "      <td>41.772000</td>\n",
       "      <td>38.060000</td>\n",
       "      <td>36.176000</td>\n",
       "      <td>33.412000</td>\n",
       "      <td>32.604000</td>\n",
       "      <td>33.268000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         1800       1850       1900       1950       2000  \\\n",
       "eight_regions                                                               \n",
       "africa_north        49.383333  51.816667  35.533333  37.983333  37.683333   \n",
       "africa_sub_saharan  43.702083  42.935417  41.112500  43.022917  44.754167   \n",
       "america_north       42.986957  41.600000  43.091304  44.095652  46.295652   \n",
       "america_south       47.550000  46.441667  45.883333  45.208333  53.250000   \n",
       "asia_west           38.096296  39.062963  35.014815  36.940741  37.222222   \n",
       "east_asia_pacific   37.363333  36.613333  37.593333  38.520000  38.623333   \n",
       "europe_east         39.000000  38.056522  34.930435  30.513043  32.239130   \n",
       "europe_west         41.772000  38.060000  36.176000  33.412000  32.604000   \n",
       "\n",
       "                         2020  \n",
       "eight_regions                  \n",
       "africa_north        36.483333  \n",
       "africa_sub_saharan  43.495833  \n",
       "america_north       44.000000  \n",
       "america_south       47.025000  \n",
       "asia_west           36.096296  \n",
       "east_asia_pacific   37.580000  \n",
       "europe_east         31.460870  \n",
       "europe_west         33.268000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eightregions_gini = gini_country_df.groupby('eight_regions')[['1800','1850','1900','1950','2000','2020']].mean()\n",
    "eightregions_gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eight_regions\n",
       "africa_north          7.192000e+06\n",
       "africa_sub_saharan    3.842090e+06\n",
       "america_north         9.775226e+06\n",
       "america_south         9.478500e+06\n",
       "asia_west             1.970827e+07\n",
       "east_asia_pacific     2.817075e+07\n",
       "europe_east           1.090022e+07\n",
       "europe_west           1.307995e+07\n",
       "Name: 1950, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_df.groupby('eight_regions')['1950'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      38000000\n",
       "1       2880000\n",
       "2      43100000\n",
       "3         77100\n",
       "4      31800000\n",
       "         ...   \n",
       "189    96500000\n",
       "190    29200000\n",
       "191    17900000\n",
       "192    14600000\n",
       "193    11100000\n",
       "Name: 2019, Length: 194, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_df['2019']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove Scientific Notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%1.0f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reset display so that scientific notation shows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('display.float_format')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Democracy Index (EIU), Electoral pluralism index, gov't index, political participation index, political culture index, civil liberties index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geo</th>\n",
       "      <th>name</th>\n",
       "      <th>time</th>\n",
       "      <th>Democracy index (EIU)</th>\n",
       "      <th>Electoral pluralism index (EIU)</th>\n",
       "      <th>Government index (EIU)</th>\n",
       "      <th>Political participation index(EIU)</th>\n",
       "      <th>Political culture index (EIU)</th>\n",
       "      <th>Civil liberties index (EIU)</th>\n",
       "      <th>Change in democracy index (EIU)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>afg</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2006</td>\n",
       "      <td>30.6</td>\n",
       "      <td>61.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>afg</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2007</td>\n",
       "      <td>30.4</td>\n",
       "      <td>56.70</td>\n",
       "      <td>3.95</td>\n",
       "      <td>22.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44.10</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>afg</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>30.2</td>\n",
       "      <td>51.70</td>\n",
       "      <td>7.90</td>\n",
       "      <td>22.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44.10</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>afg</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>27.5</td>\n",
       "      <td>38.35</td>\n",
       "      <td>7.90</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>41.15</td>\n",
       "      <td>-2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>afg</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>24.8</td>\n",
       "      <td>25.00</td>\n",
       "      <td>7.90</td>\n",
       "      <td>27.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>38.20</td>\n",
       "      <td>-2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>zwe</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>30.5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>38.9</td>\n",
       "      <td>56.3</td>\n",
       "      <td>32.40</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>zwe</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2016</td>\n",
       "      <td>30.5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>38.9</td>\n",
       "      <td>56.3</td>\n",
       "      <td>32.40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>zwe</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>31.6</td>\n",
       "      <td>5.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>44.4</td>\n",
       "      <td>56.3</td>\n",
       "      <td>32.40</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2336</th>\n",
       "      <td>zwe</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2018</td>\n",
       "      <td>31.6</td>\n",
       "      <td>5.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>44.4</td>\n",
       "      <td>56.3</td>\n",
       "      <td>32.40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>zwe</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2019</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>44.4</td>\n",
       "      <td>56.3</td>\n",
       "      <td>32.40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2338 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      geo         name  time  Democracy index (EIU)  \\\n",
       "0     afg  Afghanistan  2006                   30.6   \n",
       "1     afg  Afghanistan  2007                   30.4   \n",
       "2     afg  Afghanistan  2008                   30.2   \n",
       "3     afg  Afghanistan  2009                   27.5   \n",
       "4     afg  Afghanistan  2010                   24.8   \n",
       "...   ...          ...   ...                    ...   \n",
       "2333  zwe     Zimbabwe  2015                   30.5   \n",
       "2334  zwe     Zimbabwe  2016                   30.5   \n",
       "2335  zwe     Zimbabwe  2017                   31.6   \n",
       "2336  zwe     Zimbabwe  2018                   31.6   \n",
       "2337  zwe     Zimbabwe  2019                   31.6   \n",
       "\n",
       "      Electoral pluralism index (EIU)  Government index (EIU)  \\\n",
       "0                               61.70                    0.00   \n",
       "1                               56.70                    3.95   \n",
       "2                               51.70                    7.90   \n",
       "3                               38.35                    7.90   \n",
       "4                               25.00                    7.90   \n",
       "...                               ...                     ...   \n",
       "2333                             5.00                   20.00   \n",
       "2334                             5.00                   20.00   \n",
       "2335                             5.00                   20.00   \n",
       "2336                             5.00                   20.00   \n",
       "2337                             0.00                   25.00   \n",
       "\n",
       "      Political participation index(EIU)  Political culture index (EIU)  \\\n",
       "0                                   22.2                           25.0   \n",
       "1                                   22.2                           25.0   \n",
       "2                                   22.2                           25.0   \n",
       "3                                   25.0                           25.0   \n",
       "4                                   27.8                           25.0   \n",
       "...                                  ...                            ...   \n",
       "2333                                38.9                           56.3   \n",
       "2334                                38.9                           56.3   \n",
       "2335                                44.4                           56.3   \n",
       "2336                                44.4                           56.3   \n",
       "2337                                44.4                           56.3   \n",
       "\n",
       "      Civil liberties index (EIU)  Change in democracy index (EIU)  \n",
       "0                           44.10                              NaN  \n",
       "1                           44.10                             -0.2  \n",
       "2                           44.10                             -0.2  \n",
       "3                           41.15                             -2.7  \n",
       "4                           38.20                             -2.7  \n",
       "...                           ...                              ...  \n",
       "2333                        32.40                              2.7  \n",
       "2334                        32.40                              0.0  \n",
       "2335                        32.40                              1.1  \n",
       "2336                        32.40                              0.0  \n",
       "2337                        32.40                              0.0  \n",
       "\n",
       "[2338 rows x 10 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eiu_df = pd.read_csv(\"data_political/all.csv\")\n",
    "eiu_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Economic Data - Poverty percentages, 1.90/day, 3.20/day, & 5.50/day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 163 entries, 0 to 162\n",
      "Data columns (total 58 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   country        163 non-null    object \n",
      " 1   four_regions   163 non-null    object \n",
      " 2   eight_regions  163 non-null    object \n",
      " 3   six_regions    163 non-null    object \n",
      " 4   Latitude       163 non-null    float64\n",
      " 5   Longitude      163 non-null    float64\n",
      " 6   1967           1 non-null      float64\n",
      " 7   1968           0 non-null      float64\n",
      " 8   1969           1 non-null      float64\n",
      " 9   1970           0 non-null      float64\n",
      " 10  1971           1 non-null      float64\n",
      " 11  1972           0 non-null      float64\n",
      " 12  1973           0 non-null      float64\n",
      " 13  1974           2 non-null      float64\n",
      " 14  1975           2 non-null      float64\n",
      " 15  1976           0 non-null      float64\n",
      " 16  1977           1 non-null      float64\n",
      " 17  1978           1 non-null      float64\n",
      " 18  1979           5 non-null      float64\n",
      " 19  1980           3 non-null      float64\n",
      " 20  1981           7 non-null      float64\n",
      " 21  1982           2 non-null      float64\n",
      " 22  1983           4 non-null      float64\n",
      " 23  1984           5 non-null      float64\n",
      " 24  1985           12 non-null     float64\n",
      " 25  1986           15 non-null     float64\n",
      " 26  1987           23 non-null     float64\n",
      " 27  1988           10 non-null     float64\n",
      " 28  1989           18 non-null     float64\n",
      " 29  1990           17 non-null     float64\n",
      " 30  1991           21 non-null     float64\n",
      " 31  1992           36 non-null     float64\n",
      " 32  1993           26 non-null     float64\n",
      " 33  1994           28 non-null     float64\n",
      " 34  1995           39 non-null     float64\n",
      " 35  1996           32 non-null     float64\n",
      " 36  1997           28 non-null     float64\n",
      " 37  1998           39 non-null     float64\n",
      " 38  1999           33 non-null     float64\n",
      " 39  2000           48 non-null     float64\n",
      " 40  2001           35 non-null     float64\n",
      " 41  2002           50 non-null     float64\n",
      " 42  2003           55 non-null     float64\n",
      " 43  2004           72 non-null     float64\n",
      " 44  2005           74 non-null     float64\n",
      " 45  2006           72 non-null     float64\n",
      " 46  2007           71 non-null     float64\n",
      " 47  2008           72 non-null     float64\n",
      " 48  2009           75 non-null     float64\n",
      " 49  2010           81 non-null     float64\n",
      " 50  2011           74 non-null     float64\n",
      " 51  2012           77 non-null     float64\n",
      " 52  2013           74 non-null     float64\n",
      " 53  2014           78 non-null     float64\n",
      " 54  2015           78 non-null     float64\n",
      " 55  2016           74 non-null     float64\n",
      " 56  2017           64 non-null     float64\n",
      " 57  2018           28 non-null     float64\n",
      "dtypes: float64(54), object(4)\n",
      "memory usage: 75.1+ KB\n"
     ]
    }
   ],
   "source": [
    "poverty550.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "poverty550.drop(poverty550.iloc[:,4:39], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 163 entries, 0 to 162\n",
      "Data columns (total 23 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   country        163 non-null    object \n",
      " 1   four_regions   163 non-null    object \n",
      " 2   eight_regions  163 non-null    object \n",
      " 3   six_regions    163 non-null    object \n",
      " 4   2000           48 non-null     float64\n",
      " 5   2001           35 non-null     float64\n",
      " 6   2002           50 non-null     float64\n",
      " 7   2003           55 non-null     float64\n",
      " 8   2004           72 non-null     float64\n",
      " 9   2005           74 non-null     float64\n",
      " 10  2006           72 non-null     float64\n",
      " 11  2007           71 non-null     float64\n",
      " 12  2008           72 non-null     float64\n",
      " 13  2009           75 non-null     float64\n",
      " 14  2010           81 non-null     float64\n",
      " 15  2011           74 non-null     float64\n",
      " 16  2012           77 non-null     float64\n",
      " 17  2013           74 non-null     float64\n",
      " 18  2014           78 non-null     float64\n",
      " 19  2015           78 non-null     float64\n",
      " 20  2016           74 non-null     float64\n",
      " 21  2017           64 non-null     float64\n",
      " 22  2018           28 non-null     float64\n",
      "dtypes: float64(19), object(4)\n",
      "memory usage: 30.6+ KB\n"
     ]
    }
   ],
   "source": [
    "poverty550.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Poverty Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>four_regions</th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>six_regions</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_north</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   country four_regions       eight_regions               six_regions  \\\n",
       "0  Albania       europe         europe_east       europe_central_asia   \n",
       "1  Algeria       africa        africa_north  middle_east_north_africa   \n",
       "2   Angola       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "\n",
       "   Latitude  Longitude   2000  2001  2002  2003  ...  2009  2010   2011  \\\n",
       "0      41.0       20.0    NaN   NaN  0.02   NaN  ...   NaN   NaN    NaN   \n",
       "1      28.0        3.0    NaN   NaN   NaN   NaN  ...   NaN   NaN  0.005   \n",
       "2     -12.5       18.5  0.323   NaN   NaN   NaN  ...   NaN   NaN    NaN   \n",
       "\n",
       "    2012  2013   2014   2015   2016   2017   2018  \n",
       "0  0.011   NaN  0.019  0.013  0.011  0.017    NaN  \n",
       "1    NaN   NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "2    NaN   NaN    NaN    NaN    NaN    NaN  0.476  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty190.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eight_regions\n",
       "africa_north           1.850000\n",
       "africa_sub_saharan    50.155556\n",
       "america_north          4.350000\n",
       "america_south          4.433333\n",
       "asia_west              5.950000\n",
       "east_asia_pacific      4.930000\n",
       "europe_east            1.544444\n",
       "europe_west            0.295000\n",
       "Name: 2010, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_2010 = poverty190.groupby('eight_regions')['2010'].apply(lambda x: x.mean() * 100)\n",
    "pov190_2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pov190_2010)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the average percentage for each region for 2010-2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eight_regions</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>africa_north</th>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>11.850000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <td>50.155556</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>43.233333</td>\n",
       "      <td>29.855556</td>\n",
       "      <td>29.540000</td>\n",
       "      <td>41.683333</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>43.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_north</th>\n",
       "      <td>4.350000</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>4.085714</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>4.175000</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>3.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_south</th>\n",
       "      <td>4.433333</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>2.922222</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>3.137500</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>2.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asia_west</th>\n",
       "      <td>5.950000</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <td>4.930000</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.028571</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>1.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_east</th>\n",
       "      <td>1.544444</td>\n",
       "      <td>1.373684</td>\n",
       "      <td>1.619048</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>1.552381</td>\n",
       "      <td>1.361905</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>0.957143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_west</th>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>0.347368</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.331579</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.352632</td>\n",
       "      <td>0.331250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         2010       2011       2012       2013       2014  \\\n",
       "eight_regions                                                               \n",
       "africa_north         1.850000   0.500000   9.800000  11.850000        NaN   \n",
       "africa_sub_saharan  50.155556  43.744444  39.650000  43.233333  29.855556   \n",
       "america_north        4.350000   5.820000   8.585714   4.085714   5.200000   \n",
       "america_south        4.433333   3.866667   3.950000   2.922222   2.875000   \n",
       "asia_west            5.950000   5.183333   1.666667   2.375000   3.466667   \n",
       "east_asia_pacific    4.930000   4.380000   6.850000   6.111111   6.200000   \n",
       "europe_east          1.544444   1.373684   1.619048   1.575000   1.552381   \n",
       "europe_west          0.295000   0.345000   0.347368   0.375000   0.331579   \n",
       "\n",
       "                         2015       2016       2017       2018  \n",
       "eight_regions                                                   \n",
       "africa_north         0.750000        NaN  10.150000        NaN  \n",
       "africa_sub_saharan  29.540000  41.683333  24.666667  43.850000  \n",
       "america_north        4.680000   4.175000   4.660000   3.866667  \n",
       "america_south        2.925000   3.137500   2.533333   2.700000  \n",
       "asia_west            2.300000   2.312500   0.825000   0.900000  \n",
       "east_asia_pacific    3.028571   1.920000   2.566667   1.750000  \n",
       "europe_east          1.361905   1.255000   1.157895   0.957143  \n",
       "europe_west          0.390000   0.352632   0.331250        NaN  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_2010up = poverty190.groupby('eight_regions')[['2010','2011','2012','2013','2014','2015','2016','2017','2018']].apply(lambda x: x.mean() * 100)\n",
    "pov190_2010up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill the (blank values) NaNs with the average average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Africa_North has 3 Nans, & Europe_West has 1\n",
    "#pov190_2010up['africa_north'].fillna(pov190_2010up['africa_north'].mean())\n",
    "\n",
    "### OK, you're specifying a column that doesn't exist. 'africa_north' is a row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "## GREAT JOB!!! Use the indexing method to specifiy the row & columns to operate on, using the same data to create the\n",
    "## mean values to fillna!!! \n",
    "pov190_2010up.loc['africa_north',:].fillna(pov190_2010up.loc['africa_north',:].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same as above cell. See 3 cells up to see the NaNs that need filling\n",
    "pov190_2010up.loc['europe_west',:].fillna(pov190_2010up.loc['europe_west',:].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "pov190_2010up = pov190_2010up.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pov190_2010up' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-cf4f6ca4b145>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpov190_2010up\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pov190_2010up' is not defined"
     ]
    }
   ],
   "source": [
    "pov190_2010up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16fc005ecc0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pov190_2010up.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16fc0383828>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "\n",
    "pov190_2010up.plot(kind='line',x='eight_regions',y='2010',ax=ax)\n",
    "pov190_2010up.plot(kind='line',x='eight_regions',y='2011',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">2010</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2011</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2017</th>\n",
       "      <th colspan=\"8\" halign=\"left\">2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eight_regions</th>\n",
       "      <th>africa_north</th>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <th>america_north</th>\n",
       "      <th>america_south</th>\n",
       "      <th>asia_west</th>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <th>europe_east</th>\n",
       "      <th>europe_west</th>\n",
       "      <th>africa_north</th>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <th>...</th>\n",
       "      <th>europe_east</th>\n",
       "      <th>europe_west</th>\n",
       "      <th>africa_north</th>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <th>america_north</th>\n",
       "      <th>america_south</th>\n",
       "      <th>asia_west</th>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <th>europe_east</th>\n",
       "      <th>europe_west</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.155556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.433333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.544444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.345979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      2010                                                 \\\n",
       "eight_regions africa_north africa_sub_saharan america_north america_south   \n",
       "0                     1.85                NaN           NaN           NaN   \n",
       "1                      NaN          50.155556           NaN           NaN   \n",
       "2                      NaN                NaN          4.35           NaN   \n",
       "3                      NaN                NaN           NaN      4.433333   \n",
       "4                      NaN                NaN           NaN           NaN   \n",
       "5                      NaN                NaN           NaN           NaN   \n",
       "6                      NaN                NaN           NaN           NaN   \n",
       "7                      NaN                NaN           NaN           NaN   \n",
       "\n",
       "                                                                   \\\n",
       "eight_regions asia_west east_asia_pacific europe_east europe_west   \n",
       "0                   NaN               NaN         NaN         NaN   \n",
       "1                   NaN               NaN         NaN         NaN   \n",
       "2                   NaN               NaN         NaN         NaN   \n",
       "3                   NaN               NaN         NaN         NaN   \n",
       "4                  5.95               NaN         NaN         NaN   \n",
       "5                   NaN              4.93         NaN         NaN   \n",
       "6                   NaN               NaN    1.544444         NaN   \n",
       "7                   NaN               NaN         NaN       0.295   \n",
       "\n",
       "                      2011                     ...        2017              \\\n",
       "eight_regions africa_north africa_sub_saharan  ... europe_east europe_west   \n",
       "0                      0.5                NaN  ...         NaN         NaN   \n",
       "1                      NaN          43.744444  ...         NaN         NaN   \n",
       "2                      NaN                NaN  ...         NaN         NaN   \n",
       "3                      NaN                NaN  ...         NaN         NaN   \n",
       "4                      NaN                NaN  ...         NaN         NaN   \n",
       "5                      NaN                NaN  ...         NaN         NaN   \n",
       "6                      NaN                NaN  ...    1.157895         NaN   \n",
       "7                      NaN                NaN  ...         NaN     0.33125   \n",
       "\n",
       "                      2018                                                 \\\n",
       "eight_regions africa_north africa_sub_saharan america_north america_south   \n",
       "0                 5.816667                NaN           NaN           NaN   \n",
       "1                      NaN              43.85           NaN           NaN   \n",
       "2                      NaN                NaN      3.866667           NaN   \n",
       "3                      NaN                NaN           NaN           2.7   \n",
       "4                      NaN                NaN           NaN           NaN   \n",
       "5                      NaN                NaN           NaN           NaN   \n",
       "6                      NaN                NaN           NaN           NaN   \n",
       "7                      NaN                NaN           NaN           NaN   \n",
       "\n",
       "                                                                   \n",
       "eight_regions asia_west east_asia_pacific europe_east europe_west  \n",
       "0                   NaN               NaN         NaN         NaN  \n",
       "1                   NaN               NaN         NaN         NaN  \n",
       "2                   NaN               NaN         NaN         NaN  \n",
       "3                   NaN               NaN         NaN         NaN  \n",
       "4                   0.9               NaN         NaN         NaN  \n",
       "5                   NaN              1.75         NaN         NaN  \n",
       "6                   NaN               NaN    0.957143         NaN  \n",
       "7                   NaN               NaN         NaN    0.345979  \n",
       "\n",
       "[8 rows x 72 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_2010up.pivot(columns='eight_regions', values=['2010','2011','2012','2013','2014','2015','2016','2017','2018'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>africa_north</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>11.850000</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>5.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>50.155556</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>43.233333</td>\n",
       "      <td>29.855556</td>\n",
       "      <td>29.540000</td>\n",
       "      <td>41.683333</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>43.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>america_north</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>4.085714</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>4.175000</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>3.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>america_south</td>\n",
       "      <td>4.433333</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>2.922222</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>3.137500</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>2.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>asia_west</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>east_asia_pacific</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.028571</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>1.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>europe_east</td>\n",
       "      <td>1.544444</td>\n",
       "      <td>1.373684</td>\n",
       "      <td>1.619048</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>1.552381</td>\n",
       "      <td>1.361905</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>0.957143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>europe_west</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>0.347368</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.331579</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.352632</td>\n",
       "      <td>0.331250</td>\n",
       "      <td>0.345979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        eight_regions       2010       2011       2012       2013       2014  \\\n",
       "0        africa_north   1.850000   0.500000   9.800000  11.850000   5.816667   \n",
       "1  africa_sub_saharan  50.155556  43.744444  39.650000  43.233333  29.855556   \n",
       "2       america_north   4.350000   5.820000   8.585714   4.085714   5.200000   \n",
       "3       america_south   4.433333   3.866667   3.950000   2.922222   2.875000   \n",
       "4           asia_west   5.950000   5.183333   1.666667   2.375000   3.466667   \n",
       "5   east_asia_pacific   4.930000   4.380000   6.850000   6.111111   6.200000   \n",
       "6         europe_east   1.544444   1.373684   1.619048   1.575000   1.552381   \n",
       "7         europe_west   0.295000   0.345000   0.347368   0.375000   0.331579   \n",
       "\n",
       "        2015       2016       2017       2018  \n",
       "0   0.750000   5.816667  10.150000   5.816667  \n",
       "1  29.540000  41.683333  24.666667  43.850000  \n",
       "2   4.680000   4.175000   4.660000   3.866667  \n",
       "3   2.925000   3.137500   2.533333   2.700000  \n",
       "4   2.300000   2.312500   0.825000   0.900000  \n",
       "5   3.028571   1.920000   2.566667   1.750000  \n",
       "6   1.361905   1.255000   1.157895   0.957143  \n",
       "7   0.390000   0.352632   0.331250   0.345979  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_2010up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pov190_2010up' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-7baee3a3129d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpov190_transposed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpov190_2010up\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mpov190_transposed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pov190_2010up' is not defined"
     ]
    }
   ],
   "source": [
    "pov190_transposed = pov190_2010up.transpose()\n",
    "pov190_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>eight_regions</td>\n",
       "      <td>africa_north</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>america_north</td>\n",
       "      <td>america_south</td>\n",
       "      <td>asia_west</td>\n",
       "      <td>east_asia_pacific</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>1.85</td>\n",
       "      <td>50.1556</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.43333</td>\n",
       "      <td>5.95</td>\n",
       "      <td>4.93</td>\n",
       "      <td>1.54444</td>\n",
       "      <td>0.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.5</td>\n",
       "      <td>43.7444</td>\n",
       "      <td>5.82</td>\n",
       "      <td>3.86667</td>\n",
       "      <td>5.18333</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.37368</td>\n",
       "      <td>0.345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>9.8</td>\n",
       "      <td>39.65</td>\n",
       "      <td>8.58571</td>\n",
       "      <td>3.95</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>6.85</td>\n",
       "      <td>1.61905</td>\n",
       "      <td>0.347368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>11.85</td>\n",
       "      <td>43.2333</td>\n",
       "      <td>4.08571</td>\n",
       "      <td>2.92222</td>\n",
       "      <td>2.375</td>\n",
       "      <td>6.11111</td>\n",
       "      <td>1.575</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>5.81667</td>\n",
       "      <td>29.8556</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.875</td>\n",
       "      <td>3.46667</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.55238</td>\n",
       "      <td>0.331579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.75</td>\n",
       "      <td>29.54</td>\n",
       "      <td>4.68</td>\n",
       "      <td>2.925</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.02857</td>\n",
       "      <td>1.3619</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>5.81667</td>\n",
       "      <td>41.6833</td>\n",
       "      <td>4.175</td>\n",
       "      <td>3.1375</td>\n",
       "      <td>2.3125</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.255</td>\n",
       "      <td>0.352632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>10.15</td>\n",
       "      <td>24.6667</td>\n",
       "      <td>4.66</td>\n",
       "      <td>2.53333</td>\n",
       "      <td>0.825</td>\n",
       "      <td>2.56667</td>\n",
       "      <td>1.15789</td>\n",
       "      <td>0.33125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>5.81667</td>\n",
       "      <td>43.85</td>\n",
       "      <td>3.86667</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.345979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           index             0                   1              2  \\\n",
       "0  eight_regions  africa_north  africa_sub_saharan  america_north   \n",
       "1           2010          1.85             50.1556           4.35   \n",
       "2           2011           0.5             43.7444           5.82   \n",
       "3           2012           9.8               39.65        8.58571   \n",
       "4           2013         11.85             43.2333        4.08571   \n",
       "5           2014       5.81667             29.8556            5.2   \n",
       "6           2015          0.75               29.54           4.68   \n",
       "7           2016       5.81667             41.6833          4.175   \n",
       "8           2017         10.15             24.6667           4.66   \n",
       "9           2018       5.81667               43.85        3.86667   \n",
       "\n",
       "               3          4                  5            6            7  \n",
       "0  america_south  asia_west  east_asia_pacific  europe_east  europe_west  \n",
       "1        4.43333       5.95               4.93      1.54444        0.295  \n",
       "2        3.86667    5.18333               4.38      1.37368        0.345  \n",
       "3           3.95    1.66667               6.85      1.61905     0.347368  \n",
       "4        2.92222      2.375            6.11111        1.575        0.375  \n",
       "5          2.875    3.46667                6.2      1.55238     0.331579  \n",
       "6          2.925        2.3            3.02857       1.3619         0.39  \n",
       "7         3.1375     2.3125               1.92        1.255     0.352632  \n",
       "8        2.53333      0.825            2.56667      1.15789      0.33125  \n",
       "9            2.7        0.9               1.75     0.957143     0.345979  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_transposed.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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